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A depth map assigns a distance-related value to each accepted image location. The geometry must specify whether this means depth along the camera axis or Euclidean distance along the viewing ray. Units, missing values and the coordinate frame are also part of the representation. In calibrated stereoscopy, converting a disparity map through the camera geometry produces depth estimates.^[ref]

For an ideal camera, suppose a point has coordinates $(X,Y,Z)=(1,0,2)$ metres. Its axial depth is two metres, but its Euclidean distance from the camera center is $\sqrt{5}$ metres. The same scene can thus yield different valid numerical maps under different conventions. A missing stereo correspondence requires an invalid-location marker rather than a fabricated depth. Camera calibration and measurement uncertainty determine how confidently accepted values describe the scene.

A disparity map assigns an estimated stereo disparity to image locations having accepted matches in another view. For a horizontally rectified pair, a left-image map can storewhere $u_R(u,v)$ is the matching right-image horizontal coordinate. Constructing the map therefore requires stereo correspondence, rather than only reading individual pixel intensities. OpenCV's tutorial demonstrates block matching and shows how weak texture and spurious matches affect the result.^[ref]

For matched coordinate origins, focal length 800 pixels and camera baseline 0.12 metres, valid disparities 20 and 40 pixels imply depths 4.8 and 2.4 metres. Thus a disparity map is not itself a metric depth map: its values must be interpreted using the calibrated geometry and representation. Rejected matches need an explicit validity convention. A displayed dark patch can mean an unavailable match rather than an infinitely distant surface, and an encoded invalid value must not be blindly substituted into $Z=fB/d$.